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We propose a new framework for modelling time dependence in duration processes on financial markets. The well known autoregressive conditional duration (ACD) approach introduced by Engle and Russell (1998) will be extended in a way that allows the conditional expectation of the duration process to depend on an unobservable stochastic process, which is modelled via a Markov chain. The Markov switching ACD model (MSACD) is a very flexible tool for description and forecasting of financial duration processes. In addition the introduction of an unobservable, discrete valued regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show, that the MSACD approach is able to capture several specific characteristics of inter trade durations while alternative ACD models fail. Furthermore, we use the MSACD to test implications of a sequential trade model.
Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQ-index data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics have a clearly distinct behavior that is, for example, compatible with the well-known leverage effect. Klassifikation: C22, C51, G10
Within a two step GARCH framework we estimate the time-varying spillover effects from European and US return innovations to 10 economic sectors within the euro area, the United States, and the United Kingdom. We use daily data from January 1988 - March 2002. At the beginning of our sample sectors in all three currency areas/blocks formed a quite homogeneous group exhibiting only minor sector-specific characteristics. However, over time sectors became more heterogeneous, that is the response to aggregate shocks increasingly varies across sectors. This provides evidence that sector-specific effects gained in importance. European industries show increased heterogeneity simultaneously with the start of the European Monetary Union, whereas in the US this trend started in the early 1990's. Information technology and non-cyclical services (including telecommunication services) became the most integrated sectors worldwide, which are most affected by aggregate European and US shocks. On the other hand, basic industries, non-cyclical consumer goods, resources, and utilities became less affected by aggregate shocks. Volatility spillovers proved to be small and volatile. JEL_Klassifikation: G1, F36
Forecasting stock market volatility and the informational efficiency of the DAX-index options market
(2002)
Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility. Employing German DAX-index return data it is found that past returns do not contain useful information beyond the volatility expectations already reflected in option prices. This supports the efficient market hypothesis for the DAX-index options market.